A practical guide to using Excel for decision making, forecasting, optimization, and more


¿DESCRIPTION
This book on management science serves as a valuable resource for enhancing problem-solving and decision-making skills across various domains, including organizations and business. By reading this book, you will acquire the ability to tackle complex decisions that would otherwise be challenging. The book covers a wide array of techniques, such as profit and performance maximization, Return on Investment (ROI) optimization, as well as cost, time, and risk minimization through tools like Monte Carlo simulations and sensitivity analysis.


Throughout the book, you'll come across numerous real-life examples and case studies from diverse fields such as banking, finance, transportation, manufacturing, manpower assignment, scheduling, inventory management, and even food and product mix. The book demonstrates both linear and nonlinear techniques, utilizing Excel Solver for finding solutions. Once you grasp the usage of Solver, you'll be able to apply the learned tools effectively to address problems relevant to your background, experience, and preferences.


What sets this book apart is its hands-on approach, leveraging Excel as the primary tool for problem-solving. Rather than relying on complex mathematical formulations and algorithms, you'll learn how to set up and solve problems in a straightforward manner using Excel.


WHAT YOU WILL LEARN
¿ Learn how to set up decision making models on Excel.
¿ Solve optimization problems in the areas of business and operations.
¿ Harness the power of the Excel Solver add-in.
¿ Apply Monte Carlo simulations of risky investments using Excel.
¿ Learn how to predict future values using Excel forecasting features.


WHO THIS BOOK IS FOR
This book is for any business or operations practitioner, regardless of their role or experience level. Whether you are an analyst, a business professional, or a student, this book can help you to improve your problem-solving and decision-making skills.